Enhancing Lung Cancer Detection: Segmentation and Classification of CT Images Using U- Net with Pretrained Backbones and BIR Model

Enhancing Lung Cancer Detection: Segmentation and Classification of CT Images Using U- Net with Pretrained Backbones and BIR Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Lung Cancer Detection: Segmentation and Classification of CT Images Using U- Net with Pretrained Backbones and BIR Model Alireza Golkarieh, Farhad Bayrami, Reza Ahmadi Lashaki This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5232211/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: The aim of this study is to improve the segmentation and classification of lung regions from CT images of 78 chest cancer patients, focusing on enhancing the accuracy in detecting cancerous tissues. This research evaluates the performance of different U-Net backbone models (VGG16, ResNet50, Xception) in segmentation and employs a novel BIR-enhanced CNN model for classifying lung injury severity. Methodology: A U-Net model with three different backbones—VGG16, ResNet50, and Xception—was utilized for lung region segmentation. Preprocessing techniques such as CLAHE (Contrast Limited Adaptive Histogram Equalization) were applied to enhance contrast and image quality, followed by resizing to 128x128 pixels and normalization. For classification, a BIR-enhanced CNN model was employed to assess lung injury severity. The models were evaluated across multiple metrics, including accuracy, recall, F1 score, Intersection over Union (IoU), and Dice coefficient. Results: Among the models, VGG16 achieved the highest performance, with an accuracy of 0.9836 ± 0.0177, recall of 0.9696 ± 0.0737, F1 score of 0.9363 ± 0.0832, IoU of 0.8893 ± 0.1178, and Dice coefficient of 0.9363 ± 0.0832 in segmentation tasks. For the classification of lung injury severity, the BIR-enhanced CNN model, also utilizing VGG16, achieved a classification accuracy of 97.83%. Conclusion: This study demonstrates the significant impact of preprocessing on segmentation and classification performance. The U-Net model with the VGG16 backbone not only provides highly accurate segmentation of lung regions but also highlights cancerous areas effectively. The integration of the BIR model further improves classification accuracy, indicating that this combination offers an effective approach for lung cancer detection and diagnosis. Lung cancer U-Net Model CT segmentation Deep Learning. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5232211","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":364854796,"identity":"2b565010-3446-4c86-83f0-f3500aadf539","order_by":0,"name":"Alireza Golkarieh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYFAC5mYgIcFgwN7AwNhQAGQfAHHxAkaoFp4DQC0GxGthYDCQSCBSi7x7Y7PBzz0WcuaSjw9+nGFgl8d3gPngbR48WgzPHGxO7HkmYWw5Oy1ZcoNBcrHkAbZka7xaZiQ2H+A5IJG44XaOgeQDA+bEDQd4zKQJaTn454BE/YabZ4x/PjCoB2rh/4ZXi7xEYnMy0JYEgxs8ZkCHHQbZwoZXiwHPwWZjmQMShhvOpKVZzjA4njjzMJux5Rx8trQ3H5Z8c6BO3uD44cM3eyqqE/uONz+88QafLQcwhJjxKAfb0kBAwSgYBaNgFIwCBgCMGlPl9iqWEAAAAABJRU5ErkJggg==","orcid":"","institution":"Oakland University","correspondingAuthor":true,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Golkarieh","suffix":""},{"id":364854798,"identity":"1f69d59d-1d4b-4ae8-9996-86b9f1e59699","order_by":1,"name":"Farhad Bayrami","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Farhad","middleName":"","lastName":"Bayrami","suffix":""},{"id":364854801,"identity":"d9fc61ce-9d70-4e34-b46d-6d01ebc4023f","order_by":2,"name":"Reza Ahmadi Lashaki","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"Ahmadi","lastName":"Lashaki","suffix":""}],"badges":[],"createdAt":"2024-10-09 11:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5232211/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5232211/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66800670,"identity":"9604cb5c-60eb-4e2f-95f7-0d401d6c8500","added_by":"auto","created_at":"2024-10-16 15:02:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1547437,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptGolkarieh.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5232211/v1_covered_8cc99dbd-478a-4ffb-8df4-6cf495bb746e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Lung Cancer Detection: Segmentation and Classification of CT Images Using U- Net with Pretrained Backbones and BIR Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung cancer, U-Net Model, CT segmentation, Deep Learning.","lastPublishedDoi":"10.21203/rs.3.rs-5232211/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5232211/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003cbr\u003e\n\u003c/strong\u003eThe aim of this study is to improve the segmentation and classification of lung regions from CT images of 78 chest cancer patients, focusing on enhancing the accuracy in detecting cancerous tissues. This research evaluates the performance of different U-Net backbone models (VGG16, ResNet50, Xception) in segmentation and employs a novel BIR-enhanced CNN model for classifying lung injury severity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003cbr\u003e\n\u003c/strong\u003eA U-Net model with three different backbones—VGG16, ResNet50, and Xception—was utilized for lung region segmentation. Preprocessing techniques such as CLAHE (Contrast Limited Adaptive Histogram Equalization) were applied to enhance contrast and image quality, followed by resizing to 128x128 pixels and normalization. For classification, a BIR-enhanced CNN model was employed to assess lung injury severity. The models were evaluated across multiple metrics, including accuracy, recall, F1 score, Intersection over Union (IoU), and Dice coefficient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003cbr\u003e\n\u003c/strong\u003eAmong the models, VGG16 achieved the highest performance, with an accuracy of 0.9836 ± 0.0177, recall of 0.9696 ± 0.0737, F1 score of 0.9363 ± 0.0832, IoU of 0.8893 ± 0.1178, and Dice coefficient of 0.9363 ± 0.0832 in segmentation tasks. For the classification of lung injury severity, the BIR-enhanced CNN model, also utilizing VGG16, achieved a classification accuracy of 97.83%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003cbr\u003e\n\u003c/strong\u003eThis study demonstrates the significant impact of preprocessing on segmentation and classification performance. The U-Net model with the VGG16 backbone not only provides highly accurate segmentation of lung regions but also highlights cancerous areas effectively. The integration of the BIR model further improves classification accuracy, indicating that this combination offers an effective approach for lung cancer detection and diagnosis.\u003c/p\u003e","manuscriptTitle":"Enhancing Lung Cancer Detection: Segmentation and Classification of CT Images Using U- Net with Pretrained Backbones and BIR Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 10:04:49","doi":"10.21203/rs.3.rs-5232211/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10800135-dae0-4ea6-82ea-bc0b6c0b4779","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-16T14:53:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-14 10:04:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5232211","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5232211","identity":"rs-5232211","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}